Technical Field
The present invention relates to Recursive Neural Networks (RNN), and more particularly training RNNs in predicting future events.
Description of the Related Art
Much of the current future prediction work with RNNs focuses on detecting entailment or paraphrase relationships between two sentences or events. Work on detecting temporal relationships is needed. Some models are designed solely for predicting textual order of events instead of semantic order of events. As a consequence, some models do not model the fact that some specific events will not occur in the future as a consequence of the occurrence of a previous event. As an example, once an apple is fully consumed, it is unlikely that the next future event will be to “eat the apple.” Current models do not attempt to remove the specific events that will not occur in the future, because of the occurrence of a previous event, from the set of possible future events.
While current efforts focus on detecting entailment or paraphrase relationships between two sentences or events, there is little work on detecting temporal relationships. One line of research, tries to extract “what happens-after an event occurs” relationships from large collections of text using bootstrapping methods utilizing resources such as VerbOcean. In the context of script learning, another line of work, uses corporate statistics such as event bi-grams to define probabilities of what the next possible event will be.
However, such models cannot generalize situations of new events that have not been observed before. These models are designed for predicting textual order of events instead of semantic order of events. What is needed is an ability to train a recursive neural network (RNN) in predicting the occurrence of future events. Further, what is needed is a method for the RNN to learn how to make accurate predictions of future events and for the RNN to take action automatically in hazardous situations in order to mitigate risk to human life and damage to property.